A Switched View of Retinex: Deep Self-Regularized Low-Light Image Enhancement

• URL: http://arxiv.org/abs/2101.00603v1
• Date: Sun, 3 Jan 2021 10:40:31 GMT
• Title: A Switched View of Retinex: Deep Self-Regularized Low-Light Image Enhancement
• Authors: Zhuqing Jiang, Haotian Li, Liangjie Liu, Aidong Men, Haiying Wang
• Abstract summary: Self-regularized low-light image enhancement does not require any normal-light image in training, thereby freeing from the chains on paired or unpaired low-/normal-images. This paper presents a novel self-regularized method based on Retinex, which preserves all colors (Hue, Saturation) and only integrates Retinex theory into brightness (Value) Our method is efficient as a low-light image is decoupled into two subspaces, color and brightness, for better preservation and enhancement.
• Score: 5.217306793654357
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-regularized low-light image enhancement does not require any normal-light image in training, thereby freeing from the chains on paired or unpaired low-/normal-images. However, existing methods suffer color deviation and fail to generalize to various lighting conditions. This paper presents a novel self-regularized method based on Retinex, which, inspired by HSV, preserves all colors (Hue, Saturation) and only integrates Retinex theory into brightness (Value). We build a reflectance estimation network by restricting the consistency of reflectances embedded in both the original and a novel random disturbed form of the brightness of the same scene. The generated reflectance, which is assumed to be irrelevant of illumination by Retinex, is treated as enhanced brightness. Our method is efficient as a low-light image is decoupled into two subspaces, color and brightness, for better preservation and enhancement. Extensive experiments demonstrate that our method outperforms multiple state-of-the-art algorithms qualitatively and quantitatively and adapts to more lighting conditions.

Related papers

• LightenDiffusion: Unsupervised Low-Light Image Enhancement with Latent-Retinex Diffusion Models [39.28266945709169]
  We propose a diffusion-based unsupervised framework that incorporates physically explainable Retinex theory with diffusion models for low-light image enhancement. Experiments on publicly available real-world benchmarks show that the proposed LightenDiffusion outperforms state-of-the-art unsupervised competitors.
  arXiv  Detail & Related papers  (2024-07-12T02:54:43Z)
• Diving into Darkness: A Dual-Modulated Framework for High-Fidelity Super-Resolution in Ultra-Dark Environments [51.58771256128329]
  This paper proposes a specialized dual-modulated learning framework that attempts to deeply dissect the nature of the low-light super-resolution task. We develop Illuminance-Semantic Dual Modulation (ISDM) components to enhance feature-level preservation of illumination and color details. Comprehensive experiments showcases the applicability and generalizability of our approach to diverse and challenging ultra-low-light conditions.
  arXiv  Detail & Related papers  (2023-09-11T06:55:32Z)
• TensoIR: Tensorial Inverse Rendering [51.57268311847087]
  TensoIR is a novel inverse rendering approach based on tensor factorization and neural fields. TensoRF is a state-of-the-art approach for radiance field modeling.
  arXiv  Detail & Related papers  (2023-04-24T21:39:13Z)
• WildLight: In-the-wild Inverse Rendering with a Flashlight [77.31815397135381]
  We propose a practical photometric solution for in-the-wild inverse rendering under unknown ambient lighting. Our system recovers scene geometry and reflectance using only multi-view images captured by a smartphone. We demonstrate by extensive experiments that our method is easy to implement, casual to set up, and consistently outperforms existing in-the-wild inverse rendering techniques.
  arXiv  Detail & Related papers  (2023-03-24T17:59:56Z)
• Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement [96.09255345336639]
  We formulate a principled One-stage Retinex-based Framework (ORF) to enhance low-light images. ORF first estimates the illumination information to light up the low-light image and then restores the corruption to produce the enhanced image. Our algorithm, Retinexformer, significantly outperforms state-of-the-art methods on thirteen benchmarks.
  arXiv  Detail & Related papers  (2023-03-12T16:54:08Z)
• Invariant Descriptors for Intrinsic Reflectance Optimization [15.638996731038231]
  Intrinsic image decomposition aims to factorize an image into albedo (reflectance) and shading (illumination) sub-components. Being ill-posed and under-constrained, it is a very challenging computer vision problem. Our approach is physics-based, learning-free and leads to more accurate and robust reflectance decompositions.
  arXiv  Detail & Related papers  (2022-04-08T13:52:13Z)
• Intrinsic Image Transfer for Illumination Manipulation [1.2387676601792899]
  This paper presents a novel intrinsic image transfer (IIT) algorithm for illumination manipulation. It creates a local image translation between two illumination surfaces. We illustrate that all losses can be reduced without the necessity of taking an intrinsic image decomposition.
  arXiv  Detail & Related papers  (2021-07-01T19:12:24Z)
• Degrade is Upgrade: Learning Degradation for Low-light Image Enhancement [52.49231695707198]
  We investigate the intrinsic degradation and relight the low-light image while refining the details and color in two steps. Inspired by the color image formulation, we first estimate the degradation from low-light inputs to simulate the distortion of environment illumination color, and then refine the content to recover the loss of diffuse illumination color. Our proposed method has surpassed the SOTA by 0.95dB in PSNR on LOL1000 dataset and 3.18% in mAP on ExDark dataset.
  arXiv  Detail & Related papers  (2021-03-19T04:00:27Z)
• Low-light Image Enhancement Using the Cell Vibration Model [12.400040803969501]
  Low light very likely leads to the degradation of an image's quality and even causes visual task failures. We propose a new single low-light image lightness enhancement method. Experimental results show that the proposed algorithm is superior to nine state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-06-03T13:39:10Z)
• Unsupervised Low-light Image Enhancement with Decoupled Networks [103.74355338972123]
  We learn a two-stage GAN-based framework to enhance the real-world low-light images in a fully unsupervised fashion. Our proposed method outperforms the state-of-the-art unsupervised image enhancement methods in terms of both illumination enhancement and noise reduction.
  arXiv  Detail & Related papers  (2020-05-06T13:37:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.